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                        <h1 id="510-&#x9AD8;&#x7EA7;&#x5904;&#x7406;&#x4EA4;&#x53C9;&#x8868;&#x4E0E;&#x900F;&#x89C6;&#x8868;">5.10 &#x9AD8;&#x7EA7;&#x5904;&#x7406;-&#x4EA4;&#x53C9;&#x8868;&#x4E0E;&#x900F;&#x89C6;&#x8868;</h1>
<h2 id="&#x5B66;&#x4E60;&#x76EE;&#x6807;">&#x5B66;&#x4E60;&#x76EE;&#x6807;</h2>
<ul>
<li>&#x76EE;&#x6807; <ul>
<li>&#x5E94;&#x7528;crosstab&#x548C;pivot_table&#x5B9E;&#x73B0;&#x4EA4;&#x53C9;&#x8868;&#x4E0E;&#x900F;&#x89C6;&#x8868;</li>
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<h2 id="1-&#x4EA4;&#x53C9;&#x8868;&#x4E0E;&#x900F;&#x89C6;&#x8868;&#x4EC0;&#x4E48;&#x4F5C;&#x7528;">1 &#x4EA4;&#x53C9;&#x8868;&#x4E0E;&#x900F;&#x89C6;&#x8868;&#x4EC0;&#x4E48;&#x4F5C;&#x7528;</h2>
<p><strong>&#x63A2;&#x7A76;&#x80A1;&#x7968;&#x7684;&#x6DA8;&#x8DCC;&#x4E0E;&#x661F;&#x671F;&#x51E0;&#x6709;&#x5173;&#xFF1F;</strong></p>
<p><strong>&#x4EE5;&#x4E0B;&#x56FE;&#x5F53;&#x4E2D;&#x8868;&#x793A;&#xFF0C;week&#x4EE3;&#x8868;&#x661F;&#x671F;&#x51E0;&#xFF0C;1,0&#x4EE3;&#x8868;&#x8FD9;&#x4E00;&#x5929;&#x80A1;&#x7968;&#x7684;&#x6DA8;&#x8DCC;&#x5E45;&#x662F;&#x597D;&#x8FD8;&#x662F;&#x574F;&#xFF0C;&#x91CC;&#x9762;&#x7684;&#x6570;&#x636E;&#x4EE3;&#x8868;&#x6BD4;&#x4F8B;</strong></p>
<p><strong>&#x53EF;&#x4EE5;&#x7406;&#x89E3;&#x4E3A;&#x6240;&#x6709;&#x65F6;&#x95F4;&#x4E3A;&#x661F;&#x671F;&#x4E00;&#x7B49;&#x7B49;&#x7684;&#x6570;&#x636E;&#x5F53;&#x4E2D;&#x6DA8;&#x8DCC;&#x5E45;&#x597D;&#x574F;&#x7684;&#x6BD4;&#x4F8B;</strong></p>
<p><img src="images/&#x4EA4;&#x53C9;&#x8868;&#x900F;&#x89C6;&#x8868;&#x4F5C;&#x7528;.png" alt="&#x4EA4;&#x53C9;&#x8868;&#x900F;&#x89C6;&#x8868;&#x4F5C;&#x7528;"></p>
<p><img src="images/crosstab.png" alt="crosstab"></p>
<ul>
<li>&#x4EA4;&#x53C9;&#x8868;&#xFF1A;<strong>&#x4EA4;&#x53C9;&#x8868;&#x7528;&#x4E8E;&#x8BA1;&#x7B97;&#x4E00;&#x5217;&#x6570;&#x636E;&#x5BF9;&#x4E8E;&#x53E6;&#x5916;&#x4E00;&#x5217;&#x6570;&#x636E;&#x7684;&#x5206;&#x7EC4;&#x4E2A;&#x6570;(&#x7528;&#x4E8E;&#x7EDF;&#x8BA1;&#x5206;&#x7EC4;&#x9891;&#x7387;&#x7684;&#x7279;&#x6B8A;&#x900F;&#x89C6;&#x8868;)</strong><ul>
<li>pd.crosstab(value1, value2)</li>
</ul>
</li>
<li>&#x900F;&#x89C6;&#x8868;&#xFF1A;<strong>&#x900F;&#x89C6;&#x8868;&#x662F;&#x5C06;&#x539F;&#x6709;&#x7684;DataFrame&#x7684;&#x5217;&#x5206;&#x522B;&#x4F5C;&#x4E3A;&#x884C;&#x7D22;&#x5F15;&#x548C;&#x5217;&#x7D22;&#x5F15;&#xFF0C;&#x7136;&#x540E;&#x5BF9;&#x6307;&#x5B9A;&#x7684;&#x5217;&#x5E94;&#x7528;&#x805A;&#x96C6;&#x51FD;&#x6570;</strong><ul>
<li>data.pivot_table(&#xFF09;</li>
</ul>
</li>
<li><ul>
<li>DataFrame.pivot_table([], index=[])</li>
</ul>
</li>
</ul>
<h2 id="2-&#x6848;&#x4F8B;&#x5206;&#x6790;">2 &#x6848;&#x4F8B;&#x5206;&#x6790;</h2>
<h3 id="21-&#x6570;&#x636E;&#x51C6;&#x5907;">2.1 &#x6570;&#x636E;&#x51C6;&#x5907;</h3>
<ul>
<li>&#x51C6;&#x5907;&#x4E24;&#x5217;&#x6570;&#x636E;&#xFF0C;&#x661F;&#x671F;&#x6570;&#x636E;&#x4EE5;&#x53CA;&#x6DA8;&#x8DCC;&#x5E45;&#x662F;&#x597D;&#x662F;&#x574F;&#x6570;&#x636E;</li>
<li>&#x8FDB;&#x884C;&#x4EA4;&#x53C9;&#x8868;&#x8BA1;&#x7B97;</li>
</ul>
<pre><code class="lang-python"><span class="hljs-comment"># &#x5BFB;&#x627E;&#x661F;&#x671F;&#x51E0;&#x8DDF;&#x80A1;&#x7968;&#x5F20;&#x5F97;&#x7684;&#x5173;&#x7CFB;</span>
<span class="hljs-comment"># 1&#x3001;&#x5148;&#x628A;&#x5BF9;&#x5E94;&#x7684;&#x65E5;&#x671F;&#x627E;&#x5230;&#x661F;&#x671F;&#x51E0;</span>
date = pd.to_datetime(data.index).weekday
data[<span class="hljs-string">&apos;week&apos;</span>] = date

<span class="hljs-comment"># 2&#x3001;&#x5047;&#x5982;&#x628A;p_change&#x6309;&#x7167;&#x5927;&#x5C0F;&#x53BB;&#x5206;&#x4E2A;&#x7C7B;0&#x4E3A;&#x754C;&#x9650;</span>
data[<span class="hljs-string">&apos;posi_neg&apos;</span>] = np.where(data[<span class="hljs-string">&apos;p_change&apos;</span>] &gt; <span class="hljs-number">0</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>)

<span class="hljs-comment"># &#x901A;&#x8FC7;&#x4EA4;&#x53C9;&#x8868;&#x627E;&#x5BFB;&#x4E24;&#x5217;&#x6570;&#x636E;&#x7684;&#x5173;&#x7CFB;</span>
count = pd.crosstab(data[<span class="hljs-string">&apos;week&apos;</span>], data[<span class="hljs-string">&apos;posi_neg&apos;</span>])
</code></pre>
<p>&#x4F46;&#x662F;&#x6211;&#x4EEC;&#x770B;&#x5230;count&#x53EA;&#x662F;&#x6BCF;&#x4E2A;&#x661F;&#x671F;&#x65E5;&#x5B50;&#x7684;&#x597D;&#x574F;&#x5929;&#x6570;&#xFF0C;&#x5E76;&#x6CA1;&#x6709;&#x5F97;&#x5230;&#x6BD4;&#x4F8B;&#xFF0C;&#x8BE5;&#x600E;&#x4E48;&#x53BB;&#x505A;&#xFF1F;</p>
<ul>
<li>&#x5BF9;&#x4E8E;&#x6BCF;&#x4E2A;&#x661F;&#x671F;&#x4E00;&#x7B49;&#x7684;&#x603B;&#x5929;&#x6570;&#x6C42;&#x548C;&#xFF0C;&#x8FD0;&#x7528;&#x9664;&#x6CD5;&#x8FD0;&#x7B97;&#x6C42;&#x51FA;&#x6BD4;&#x4F8B;</li>
</ul>
<pre><code class="lang-python"><span class="hljs-comment"># &#x7B97;&#x6570;&#x8FD0;&#x7B97;&#xFF0C;&#x5148;&#x6C42;&#x548C;</span>
sum = count.sum(axis=<span class="hljs-number">1</span>).astype(np.float32)

<span class="hljs-comment"># &#x8FDB;&#x884C;&#x76F8;&#x9664;&#x64CD;&#x4F5C;&#xFF0C;&#x5F97;&#x51FA;&#x6BD4;&#x4F8B;</span>
pro = count.div(sum, axis=<span class="hljs-number">0</span>)
</code></pre>
<h3 id="22-&#x67E5;&#x770B;&#x6548;&#x679C;">2.2 &#x67E5;&#x770B;&#x6548;&#x679C;</h3>
<p>&#x4F7F;&#x7528;plot&#x753B;&#x51FA;&#x8FD9;&#x4E2A;&#x6BD4;&#x4F8B;&#xFF0C;&#x4F7F;&#x7528;stacked&#x7684;&#x67F1;&#x72B6;&#x56FE;</p>
<pre><code class="lang-python">pro.plot(kind=<span class="hljs-string">&apos;bar&apos;</span>, stacked=<span class="hljs-keyword">True</span>)
plt.show()
</code></pre>
<h3 id="23-&#x4F7F;&#x7528;pivottable&#x900F;&#x89C6;&#x8868;&#x5B9E;&#x73B0;">2.3 &#x4F7F;&#x7528;pivot_table(&#x900F;&#x89C6;&#x8868;)&#x5B9E;&#x73B0;</h3>
<p>&#x4F7F;&#x7528;&#x900F;&#x89C6;&#x8868;&#xFF0C;&#x521A;&#x624D;&#x7684;&#x8FC7;&#x7A0B;&#x66F4;&#x52A0;&#x7B80;&#x5355;</p>
<pre><code class="lang-python"><span class="hljs-comment"># &#x901A;&#x8FC7;&#x900F;&#x89C6;&#x8868;&#xFF0C;&#x5C06;&#x6574;&#x4E2A;&#x8FC7;&#x7A0B;&#x53D8;&#x6210;&#x66F4;&#x7B80;&#x5355;&#x4E00;&#x4E9B;</span>
data.pivot_table([<span class="hljs-string">&apos;posi_neg&apos;</span>], index=<span class="hljs-string">&apos;week&apos;</span>)
</code></pre>
<h2 id="3-&#x5C0F;&#x7ED3;">3 &#x5C0F;&#x7ED3;</h2>
<ul>
<li>&#x4EA4;&#x53C9;&#x8868;&#x4E0E;&#x900F;&#x89C6;&#x8868;&#x7684;&#x4F5C;&#x7528;&#x3010;&#x77E5;&#x9053;&#x3011;<ul>
<li>&#x4EA4;&#x53C9;&#x8868;&#xFF1A;&#x8BA1;&#x7B97;&#x4E00;&#x5217;&#x6570;&#x636E;&#x5BF9;&#x4E8E;&#x53E6;&#x5916;&#x4E00;&#x5217;&#x6570;&#x636E;&#x7684;&#x5206;&#x7EC4;&#x4E2A;&#x6570;</li>
<li>&#x900F;&#x89C6;&#x8868;&#xFF1A;&#x6307;&#x5B9A;&#x67D0;&#x4E00;&#x5217;&#x5BF9;&#x53E6;&#x4E00;&#x5217;&#x7684;&#x5173;&#x7CFB;</li>
</ul>
</li>
</ul>

                    
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